Multiple Texture Boltzmann Machines

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We assess the generative power of the mPoT-model of [10] with tiled-convolutional weight sharing as a model for visual textures by specifically training on this task, evaluating model performance on texture synthesis and inpainting tasks using quantitative metrics. We also analyze the relative importance of the mean and covariance parts of the mPoT model by comparing its performance to those of its subcomponents, tiled-convolutional versions of the PoT/FoE and Gaussian-Bernoulli restricted Boltzmann machine (GB-RBM). Our results suggest that while state-of-the-art or better performance can be achieved using the mPoT, similar performance can be achieved with the mean-only model. We then develop a model for multiple textures based on the GB-RBM, using a shared set of weights but texture-specific hidden unit biases. We show comparable performance of the multiple texture model to individually trained texture models.
Original languageEnglish
Title of host publicationProceedings AISTATS 2012
PublisherJournal of Machine Learning Research: Workshop and Conference Proceedings
Pages638-646
Number of pages9
Publication statusPublished - 2012

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